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一种基于预测和整合的理解神经认知模型。

A neuro-cognitive model of comprehension based on prediction and unification.

作者信息

Blache Philippe

机构信息

Laboratoire Parole et Langage (LPL-CNRS), Aix-en-Provence, France.

Institute of Language, Communication and the Brain (ILCB), Marseille, France.

出版信息

Front Hum Neurosci. 2024 Apr 9;18:1356541. doi: 10.3389/fnhum.2024.1356541. eCollection 2024.

Abstract

Most architectures and models of language processing have been built upon a restricted view of language, which is limited to sentence processing. These approaches fail to capture one primordial characteristic: efficiency. Many facilitation effects are known to be at play in natural situations such as conversation (shallow processing, no real access to the lexicon, etc.) without any impact on the comprehension. In this study, on the basis of a new model integrating into a unique architecture, we present these facilitation effects for accessing the meaning into the classical compositional architecture. This model relies on two mechanisms, prediction and unification, and provides a unique architecture for the description of language processing in its natural environment.

摘要

大多数语言处理的架构和模型都是基于对语言的一种受限观点构建的,这种观点仅限于句子处理。这些方法未能捕捉到一个基本特征:效率。在诸如对话等自然情境中,许多促进效应在发挥作用(浅层处理,无法真正访问词汇表等),而对理解没有任何影响。在本研究中,基于一个整合到独特架构中的新模型,我们将这些用于获取意义的促进效应呈现到经典的组合架构中。该模型依赖于预测和统一两种机制,并为描述自然环境中的语言处理提供了一个独特的架构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97f1/11035797/7d4e4d36f345/fnhum-18-1356541-g0001.jpg

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